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Change Management Part 1: The Big Picture

Dennis Drogseth

This is the first of a three-part series on change management. In this blog, I’ll try to answer the question, “What is change management?” from both a process and a benefits (or use-case) perspective.

In the second installment, I’ll address best practices for both planning for and measuring the success of change management initiatives. I’ll also examine some of the issues that EMA has seen arise when IT organizations try to establish a more cohesive cross-domain approach to managing change. In part three, I’ll focus on the impacts of cloud, agile, and mobile, including the growing need for investments in automation and analytics to make change management more effective.

Change Management Processes

Like many words and concepts in English language, especially when applied to technology, “change management” carries with it a wide variety of associations. In terms of the processes established in the IT Infrastructure Library (ITIL), change management is best understood as a strategic approach to planning for change.

ITIL defines change management succinctly as, “the process responsible for controlling the lifecycle of all changes, enabling beneficial changes to be made with minimum disruption to IT Services.” As such, change management is a logical system of governance that addresses a set of relevant questions, which include the following:

■ Who requested the change?

■ What is the reason for the change?

■ What is the desired result of the change?

■ What are the risks involved with making the change?

■ What resources are required to deliver the change?

■ Who is responsible for the build, test, and implementation of the change?

■ What is the relationship between this change and other changes?

But this system of governance doesn’t stand alone. Actually implementing and managing changes requires attention to other ITIL processes. These include (but are not limited to):

■ Service asset and configuration management (SACM) – “The process responsible for maintaining information about configuration items required to deliver an IT Service, including their relationships.” SACM addresses how IT hardware and software assets (including applications) have been configured and, even more critically, identifies the relationships and interdependencies affecting infrastructure and application assets.

■ Release and deployment management – “The process responsible for planning, scheduling and controlling the build, test and deployment of releases, and for delivering new functionality required by the business while protecting the integrity of existing services.” As you can imagine, release management and automation should go hand in hand.

There are other ITIL processes relevant to managing change effectively, including capacity management, problem management, availability management, and continual service improvement, just to name a few. From just this brief snapshot, you might get the (correct) impression that change management in the “big picture” is at the very heart of effective IT operations. If done correctly, change management touches all of IT—including the service desk, operational teams, development, the executive suite, and even non-IT service consumers. This central position makes change management both an opportunity and a challenge.

Change Management Use Cases

Image removed.Probably the best way to understand the “change management opportunity” is to look at some of the use cases affiliated with it. Effective change management can empower a wide range of other initiatives, from lifecycle asset management to DevOps, service impact management, and improved service performance. EMA consultants have estimated that more than 60% of IT service disruptions come from the impacts of changes made across the application infrastructure—and this estimate is conservative compared to some of the other industry estimates I’ve seen. Having good change management processes and technologies in place is also a foundation for better automation, as well as for better optimization of both public and private cloud resources. And the list goes on.

Even the list below, derived in large part from CMDB Systems: Making Change Work in the Age of Cloud and Agile, is a partial one, but it should provide a useful departure point for your planning—as you seek to prioritize the use case(s) most relevant to you.

■ Governance and compliance: Managing change to conform with critical industry, security, and asset-related requirements for compliance, while minimizing change-related disruptions. This, can provide significant financial benefits including OpEx savings, superior service availability, improved security and savings from avoiding the penalty costs incurred when changes are made poorly.

■ Data center consolidation—mergers and acquisitions: Planning new options for data center consolidation is definitely on the rise, and mergers and acquisitions often lead to data center consolidation initiatives. Effective change management can shorten consolidation time, minimize costs, and improve the quality of the outcome.

■ Disaster recovery – Disaster recovery initiatives may be an extension of data center consolidation, or they may be independent. Automating change for disaster recovery is one of the more common drivers for a more systemic approach to change management.

■ The proverbial “move to cloud” – The stunning rise of virtualization and the persistent move to assimilate both internal and public cloud options make change impact management and effective change automation essential.

■ Facilities management and Green IT – This use case requires dynamic insights into both configuration and “performance”-related attributes for configuration items (CIs), both internal to IT (servers, switches, desktops, etc.) and external to traditional IT boundaries (facilities, power, etc.).

■ Optimizing the end-user experience across heterogeneous endpoints – Meeting the challenges of unified endpoint management including mobile endpoints, requires a flexible adoption of change management best practices and automation. But the benefits of doing this can be significant—impacting asset management, security, and financial optimization, while increasing end-user satisfaction with IT services.

Change Management Part 2

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As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...

According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...

2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...

Regardless of OpenShift being a scalable and flexible software, it can be a pain to monitor since complete visibility into the underlying operations is not guaranteed ... To effectively monitor an OpenShift environment, IT administrators should focus on these five key elements and their associated metrics ...

Change Management Part 1: The Big Picture

Dennis Drogseth

This is the first of a three-part series on change management. In this blog, I’ll try to answer the question, “What is change management?” from both a process and a benefits (or use-case) perspective.

In the second installment, I’ll address best practices for both planning for and measuring the success of change management initiatives. I’ll also examine some of the issues that EMA has seen arise when IT organizations try to establish a more cohesive cross-domain approach to managing change. In part three, I’ll focus on the impacts of cloud, agile, and mobile, including the growing need for investments in automation and analytics to make change management more effective.

Change Management Processes

Like many words and concepts in English language, especially when applied to technology, “change management” carries with it a wide variety of associations. In terms of the processes established in the IT Infrastructure Library (ITIL), change management is best understood as a strategic approach to planning for change.

ITIL defines change management succinctly as, “the process responsible for controlling the lifecycle of all changes, enabling beneficial changes to be made with minimum disruption to IT Services.” As such, change management is a logical system of governance that addresses a set of relevant questions, which include the following:

■ Who requested the change?

■ What is the reason for the change?

■ What is the desired result of the change?

■ What are the risks involved with making the change?

■ What resources are required to deliver the change?

■ Who is responsible for the build, test, and implementation of the change?

■ What is the relationship between this change and other changes?

But this system of governance doesn’t stand alone. Actually implementing and managing changes requires attention to other ITIL processes. These include (but are not limited to):

■ Service asset and configuration management (SACM) – “The process responsible for maintaining information about configuration items required to deliver an IT Service, including their relationships.” SACM addresses how IT hardware and software assets (including applications) have been configured and, even more critically, identifies the relationships and interdependencies affecting infrastructure and application assets.

■ Release and deployment management – “The process responsible for planning, scheduling and controlling the build, test and deployment of releases, and for delivering new functionality required by the business while protecting the integrity of existing services.” As you can imagine, release management and automation should go hand in hand.

There are other ITIL processes relevant to managing change effectively, including capacity management, problem management, availability management, and continual service improvement, just to name a few. From just this brief snapshot, you might get the (correct) impression that change management in the “big picture” is at the very heart of effective IT operations. If done correctly, change management touches all of IT—including the service desk, operational teams, development, the executive suite, and even non-IT service consumers. This central position makes change management both an opportunity and a challenge.

Change Management Use Cases

Image removed.Probably the best way to understand the “change management opportunity” is to look at some of the use cases affiliated with it. Effective change management can empower a wide range of other initiatives, from lifecycle asset management to DevOps, service impact management, and improved service performance. EMA consultants have estimated that more than 60% of IT service disruptions come from the impacts of changes made across the application infrastructure—and this estimate is conservative compared to some of the other industry estimates I’ve seen. Having good change management processes and technologies in place is also a foundation for better automation, as well as for better optimization of both public and private cloud resources. And the list goes on.

Even the list below, derived in large part from CMDB Systems: Making Change Work in the Age of Cloud and Agile, is a partial one, but it should provide a useful departure point for your planning—as you seek to prioritize the use case(s) most relevant to you.

■ Governance and compliance: Managing change to conform with critical industry, security, and asset-related requirements for compliance, while minimizing change-related disruptions. This, can provide significant financial benefits including OpEx savings, superior service availability, improved security and savings from avoiding the penalty costs incurred when changes are made poorly.

■ Data center consolidation—mergers and acquisitions: Planning new options for data center consolidation is definitely on the rise, and mergers and acquisitions often lead to data center consolidation initiatives. Effective change management can shorten consolidation time, minimize costs, and improve the quality of the outcome.

■ Disaster recovery – Disaster recovery initiatives may be an extension of data center consolidation, or they may be independent. Automating change for disaster recovery is one of the more common drivers for a more systemic approach to change management.

■ The proverbial “move to cloud” – The stunning rise of virtualization and the persistent move to assimilate both internal and public cloud options make change impact management and effective change automation essential.

■ Facilities management and Green IT – This use case requires dynamic insights into both configuration and “performance”-related attributes for configuration items (CIs), both internal to IT (servers, switches, desktops, etc.) and external to traditional IT boundaries (facilities, power, etc.).

■ Optimizing the end-user experience across heterogeneous endpoints – Meeting the challenges of unified endpoint management including mobile endpoints, requires a flexible adoption of change management best practices and automation. But the benefits of doing this can be significant—impacting asset management, security, and financial optimization, while increasing end-user satisfaction with IT services.

Change Management Part 2

Hot Topics

The Latest

As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...

According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...

2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...

Regardless of OpenShift being a scalable and flexible software, it can be a pain to monitor since complete visibility into the underlying operations is not guaranteed ... To effectively monitor an OpenShift environment, IT administrators should focus on these five key elements and their associated metrics ...